Prevalence of distributed networks consisting of devices such as mobile phones and sensors with growing computational and storage capability enables pushing more network computations to the edge. Federated learning has emerged as a promising framework to train machine learning models under orchestration of a central server while training data remains distributed among the edge devices which are called clients [1]}. In federated learning, a central server sends the current model to a set of clients at each learning round. Participating clients then compute updates of the current model based on their local data and send these updates to the server instead of their local data. The server then update the model. This procedure continues until convergence. In this context, communication efficiency is of utmost importance. To this end, clients-to-server
communication efficiency in federated learning has been studied extensively in the context of model updates compression, see e.g., [2]}, [3]}.
On the other hand, server-to-clients communication bottleneck arises if the learning task involves large model, such that the required bandwidth to transmit the model exceeds the available bandwidth for server-to-clients communication. For example, base stations can be employed as an aggregating server in certain applications [4]}. Often times the base station may only assign a limited portion of available bandwidth for server-to-client communication, while reserving most of the available bandwidth for other service required by users. Meanwhile, large models trained at the central server may exceed the clients' memory capacity. These challenges all motivate the study of server-to-clients communication-efficient federated learning, which is also the focus of the present work.
